Abstract
Game theory is an interdisciplinary approach to the study of human behavior. Games describe a widely accepted framework for representing interactive decision-making. Artificial Neural Networks (ANNs) are universal approximators and have the ability of learning. Combining ANNs with game representation, we introduced a new architecture by which the learning abilities of ANNs are utilized to predict game behavior. Based on previous work, we investigated further the potential value of neural networks for modeling and predicting human interactive learning in repeated games. We conducted simulation studies based on the new model using experiments data which are provided by authors other than this paper. Through computer simulations and comparing with other models, we demonstrated that our model is superior in many respects to other models on ten experiments.
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Sun, Q., Ren, G., Qi, X. (2009). Interactive Learning Neural Networks for Predicting Game Behavior. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_87
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DOI: https://doi.org/10.1007/978-3-642-01507-6_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01506-9
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